Artificial intelligence systems and methods for vibrant constitutional guidance

ABSTRACT

An artificial intelligence advisory system for vibrant constitutional guidance includes a diagnostic engine operating on at least a server and configured to receive at least a biological extraction from a user and generate a diagnostic output, based on the at least a biological extraction, including at least a prognostic label and at least an ameliorative process label. The system includes a plan generation module configured to generate, based on the diagnostic output, a comprehensive instruction set, associated with the user, including at least a current prognostic descriptor and at least an ameliorative process descriptor. The system includes client-interface module designed and configured to transmit the comprehensive instruction set to at least a user client device. The system includes at least an advisory module configured to generate at least an advisory output as a function of the comprehensive instruction set and transmit the advisory output to at least an advisor client device.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed toartificial intelligence systems and methods for vibrant constitutionalguidance.

BACKGROUND

Historically, Western medicine and professionals in the industry haveapproached the practice from a reaction-based perspective wherein apatient visits a physician upon discovering an illness or afterencountering an unconventional issue, and the physician subsequentlyexamines, diagnoses, and treats the patient. This practice is rooted inan infrastructure that seeks to ensure that physicians apply variousmedications and treatments in order to provide immediate attention tomedical issues sustained by the patient.

However, this approach is not focused on the concept of increasing lifevibrancy and longevity and is also inefficient for those who not onlyprefer to not be treated with conventional medical treatment plans, butalso those who prefer a curative and life-extending approach to theirmedical care and lifestyle in which the patient is armed with aplentiful amount of information regarding their physical and mentalaptitude, genetic disadvantages, overall physical condition, and otheruseful information acquired prior to the presence of an illness or otherissue allowing them to make choices regarding their health andlifestyle.

Additional drawbacks to the current Western medicine approach includethe lack of ability to develop patient specific strategies that accountfor various aspects of the patient's life, such as genetics, physicalfitness, dietary restrictions, and general habits. For example, apatient would have no means of knowing about personal health issues suchas allergies prior to falling victim to the specific allergen andsubsequently being clinically diagnosed. After being armed with thisinformation, the patient is finally able to make lifestyle changes inorder to avoid contact with the allergen, but by this point the patienthas already endured the ramifications of being exposed to the allergen.

Furthermore, the current methods, systems, and processes of Westernmedicine do not allow an opportunity for personalization of healthcareoptions specific to the respective patient. For example, most medicalinsurance companies provide options that are able to facilitate basicmedical care, but methods and procedures that require any type ofsupplemental testing specific to the patient such as genetic testing aretypically outside of the insurance coverage resulting in the patienthaving to pay out of pocket or refrain from receiving said services. Dueto this limitation, there is a lack of incentive to collect massquantities of patient-specific information for analyzation anddiagnostic purposes, much less develop a supporting infrastructure tohouse and utilize the collected patient data for generating a plan thatpromotes vibrant health and longevity. Drawbacks such as these arecounterintuitive to the concept of using preemptive measures toaccomplish vibrant health such as genetic testing, nutrition plans,wellness coaching, and countless other personalized measures.

SUMMARY OF THE DISCLOSURE

In an aspect, an artificial intelligence advisory system for vibrantconstitutional guidance includes at least a server. The system includesa diagnostic engine operating on the at least a server, wherein thediagnostic engine is configured to receive at least a biologicalextraction from a user and generate a diagnostic output based on the atleast a biological extraction, the diagnostic output including at leasta prognostic label and at least an ameliorative process label. Thesystem includes a plan generation module operating on the at least aserver, the plan generation module designed and configured to generate,based on the diagnostic output, a comprehensive instruction setassociated with the user, wherein the comprehensive instruction setincludes at least a current prognostic descriptor and at least anameliorative process descriptor. The system includes a client-interfacemodule, the client-interface module designed and configured to transmitthe comprehensive instruction set to at least a user client deviceassociated with the user. The system includes at least an advisorymodule, the at least an advisory module designed and configured togenerate at least an advisory output as a function of the comprehensiveinstruction set and transmit the advisory output to at least an advisorclient device.

In another aspect, a method of vibrant constitutional guidance usingartificial intelligence. The method includes receiving, by a diagnosticengine operating on at least a server, at least a biological extractionfrom a user. The method includes generating, by the diagnostic engine, adiagnostic output based on the at least a biological extraction, thediagnostic output including at least a prognostic label and at least anameliorative process label. The method includes generating, by a plangeneration module operating on the at least a server, and based on thediagnostic output, a comprehensive instruction set associated with theuser, the comprehensive instruction set including at least anameliorative process descriptor and at least an ameliorative processdescriptor. The method includes transmitting, by a client-interfacemodule operating on the at least a server, the comprehensive instructionset to at least a user client device associated with the user. Themethod includes generating, by at least an advisory module, at least anadvisory output as a function of the comprehensive instruction set.

The systems and methods described herein provide improvements to theprocessing, storage, and utility of data collected along with acentralized vibrant constitutional network configured to developcomprehensive plans for users, and execute processes and services basedon components of the comprehensive plans. By using a rule-based model ora machine-learned model, one or more analyses are performed on collecteddata, and outputs of training data are generated based on the one ormore analyses on the collected data. The outputs are used to generatecomprehensive plans, and the vibrant constitutional network is able toprovide users with not only personalized information associated withvarious components of their lives, but more importantly the ability tomake decisions that support vibrant health and longevity influenced bythe plurality of information based on the collected data. Furthermore,the systems and methods provide an unconventional use of the pluralityof collected data via automatic execution of processes and services bythe vibrant constitutional network based on generated comprehensiveplans. Thus, the systems and methods described herein improve thefunctioning of computing systems by optimizing big data processing andimproving the utility of the processed big data via its unconventionalapplication, but most importantly the system and methods improve overallhealth and lifestyle via the centralized network promoting vibrant lifeand longevity.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisory system for vibrant constitutionalguidance;

FIG. 2 is a block diagram illustrating an exemplary embodiment of adiagnostic engine;

FIG. 3 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 4 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 9 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner and associated system elements;

FIG. 10 is a block diagram illustrating an exemplary embodiment of aplan generator module and associated system elements;

FIG. 11 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 12 is a block diagram illustrating an exemplary embodiment of anameliorative process label classification database;

FIG. 13 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 16 is a block diagram illustrating an exemplary embodiment of anadvisory module and associated system elements;

FIG. 17 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisor and associated system elements;

FIG. 18 is a block diagram illustrating an exemplary embodiment of adefault response database;

FIG. 19 is a flow diagram illustrating an exemplary method of vibrantconstitutional guidance using artificial intelligence; and

FIG. 20 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Systems and methods are provided for a vibrant constitutional networkconfigured to interact with a plurality of applicable processes andservices associated with users of the vibrant constitutional network.Based on the collection of data associated with a user and data acquiredfrom one or more creditable sources, a plurality of data includingphysiological and essential medical, wellness, or lifestyle data isacquired, anatomized, transmitted, and stored in the vibrant healthnetwork configured to continuously update and enhance the collecteddata. Based on one or more diagnostics derived from analyses of thecollected data, one or more comprehensive plans associated with the useris generated comprising a plurality of diagnostic data that may include,but is not limited to biomarkers, genetic deficiencies, nutrition,personal statistics related to general welfare, and other relevantinformation. Information within the vibrant constitutional network andthe comprehensive plans may be used to execute processes and servicesassociated with but not limited to nutrition (food and supplements),healthcare professional selection, health & nutritioncoaching/mentoring, social networking, insurance selection(preventative, regenerative, and catastrophic), diagnostics (medical andpersonal), and any other applicable area.

Turning now to FIG. 1, an artificial intelligence advisory system 100for vibrant constitutional guidance. Artificial intelligence advisorysystem includes at least a server 104. At least a server 104 may includeany computing device as described below in reference to FIG. 20,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described belowin reference to FIG. 20. At least a server 104 may be housed with, maybe incorporated in, or may incorporate one or more sensors of at least asensor. Computing device may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. At leasta server 104 may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. At least a server 104 with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting a at least a server 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. At least a server 104 mayinclude but is not limited to, for example, a at least a server 104 orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. At least aserver 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. At least a server 104 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. At least a server 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

Still referring to FIG. 1, system 100 includes a diagnostic engine 108operating on the at least a server 104, wherein the diagnostic engine108 configured to receive at least a biological extraction from a userand generate a diagnostic output, the diagnostic output including atleast a prognostic label and at least an ameliorative process label. Atleast a server 104, diagnostic engine 108, and/or one or more modulesoperating thereon may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, at least a server 104 and/or diagnostic engine108 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. At least aserver 104 and/or diagnostic engine 108 may perform any step or sequenceof steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 2, at least a server 104 and/or diagnostic engine108 may be designed and configured to receive training data. Trainingdata, as used herein, is data containing correlation that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 2, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 2, categorization device may be configured toreceive a first training set 200 including a plurality of first dataentries, each first data entry of the first training set 200 includingat least an element of physiological state data 204 and at least acorrelated first prognostic label 208. At least an element ofphysiological state data 204 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 204 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 204 may include, without limitation, immune function data such asInterleukine-6 TNF-alpha, systemic inflammatory cytokines, and the like.

Continuing to refer to FIG. 2, physiological state data 204 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 204 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data 204 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 204 may includemeasures of estimated glomerular filtration rate (eGFR.). Physiologicalstate data 204 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin D, 25 hydroxy, blood urea nitrogen,creatinine, sodium, potassium, chloride, carbon dioxide, uric acid,albumin, globulin, calcium, phosphorus, alkaline photophatase, alanineamino transferase, aspartate amino transferase, lactate dehydrogenase(LDH), bilirubin, gamma-glutamyl transferase (GGT), iron, and/or totaliron binding capacity (TIBC), or the like. Physiological state data 204may include antinuclear antibody levels. Physiological state data 204may include aluminum levels. Physiological state data 204 may includearsenic levels. Physiological state data 204 may include levels offibronigen, plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 2, physiological state data 204 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 204 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 204 may include a measure of waist circumference.Physiological state data 204 may include body mass index (BMI).Physiological state data 204 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 204 may include one or more measures of musclemass. Physiological state data 204 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 2, physiological state data 204 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain. Physiological state data 204 may include genomic data,including deoxyribonucleic acid (DNA) samples and/or sequences, such aswithout limitation DNA sequences contained in one or more chromosomes inhuman cells. Genomic data may include, without limitation, ribonucleicacid (RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 204 may include proteomic data, whichas used herein is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 204 may include data concerninga microbiome of a person, which as used herein includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 204 of a person, and/or on prognostic labelsand/or ameliorative processes as described in further detail below.Physiological state data 204 may include any physiological state data204, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like. Examples of physiological state data204 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 204 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 2, each element of first training set 200includes at least a first prognostic label 208. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or heathy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 204 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, Leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 2, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 2, in each first data element of firsttraining set 200, at least a first prognostic label 208 of the dataelement is correlated with at least an element of physiological statedata 204 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe first training set 200. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 200 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 2, diagnostic engine 108may be designed and configured to associate at least an element ofphysiological state data 204 with at least a category from a list ofsignificant categories of physiological state data 204. Significantcategories of physiological state data 204 may include labels and/ordescriptors describing types of physiological state data 204 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 204 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction; chronic inflammation; anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly; measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance; blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 2, diagnostic engine 108 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 108 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 108 and/or a user device connected to diagnosticengine 108 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Referring again to FIG. 2, data information describing significantcategories of physiological data, relationships of such categories toprognostic labels, and/or significant categories of prognostic labelsmay alternatively or additionally be extracted from one or moredocuments using a language processing module 216. Language processingmodule 216 may include any hardware and/or software module. Languageprocessing module 216 may be configured to extract, from the one or moredocuments, one or more words. One or more words may include, withoutlimitation, strings of one or characters, including without limitationany sequence or sequences of letters, numbers, punctuation, diacriticmarks, engineering symbols, geometric dimensioning and tolerancing(GD&T) symbols, chemical symbols and formulas, spaces, whitespace, andother symbols, including any symbols usable as textual data as describedabove. Textual data may be parsed into tokens, which may include asimple word (sequence of letters separated by whitespace) or moregenerally a sequence of characters as described previously. The term“token,” as used herein, refers to any smaller, individual groupings oftext from a larger source of text; tokens may be broken up by word, pairof words, sentence, or other delimitation. These tokens may in turn beparsed in various ways. Textual data may be parsed into words orsequences of words, which may be considered words as well. Textual datamay be parsed into “n-grams”, where all sequences of n consecutivecharacters are considered. Any or all possible sequences of tokens orwords may be stored as “chains”, for example for use as a Markov chainor Hidden Markov Model.

Still referring to FIG. 2, language processing module 216 may compareextracted words to categories of physiological data recorded atdiagnostic engine 108, one or more prognostic labels recorded atdiagnostic engine 108, and/or one or more categories of prognosticlabels recorded at diagnostic engine 108; such data for comparison maybe entered on diagnostic engine 108 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 216 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine108 and/or language processing module 216 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 108, or the like.

Still referring to FIG. 2, language processing module 216 and/ordiagnostic engine 108 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 216may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 2, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 2, language processing module 216 may use acorpus of documents to generate associations between language elementsin a language processing module 216, and diagnostic engine 108 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 108 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into diagnostic engine 108. Documents may beentered into diagnostic engine 108 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, diagnostic engine 108 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 2, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of biological extraction, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 2, diagnostic engine 108 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 2, in an embodiment, diagnostic engine 108may be configured, for instance as part of receiving the first trainingset 200, to associate at least correlated first prognostic label 208with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 108 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 2, diagnostic engine 108 is designed andconfigured to receive a second training set 220 including a plurality ofsecond data entries. Each second data entry of the second training set220 includes at least a second prognostic label 224; at least a secondprognostic label 224 may include any label suitable for use as at leasta first prognostic label 208 as described above. Each second data entryof the second training set 220 includes at least an ameliorative processlabel 228 correlated with the at least a second prognostic label 224,where correlation may include any correlation suitable for correlationof at least a first prognostic label 208 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 228 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Continuing to refer to FIG. 2, in an embodiment diagnostic engine 108may be configured, for instance as part of receiving second training set220, to associate the at least second prognostic label 224 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 208.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set200 according to a first process as described above and for prognosticlabels in second training set 220 according to a second process asdescribed above.

Still referring to FIG. 2, diagnostic engine 108 may be configured, forinstance as part of receiving second training set 220, to associate atleast a correlated ameliorative process label 228 with at least acategory from a list of significant categories of ameliorative processlabels 228. In an embodiment, diagnostic engine 108 and/or a user deviceconnected to diagnostic engine 108 may provide a second graphical userinterface 232 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 216 or the like as described above.

In an embodiment, and still referring to FIG. 2, diagnostic engine 108may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 108 may be configured, for instanceas part of receiving second training set 220, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 228; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 228, and/orefficacy of ameliorative process labels 228 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 216 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 2, diagnostic engine 108 may beconfigured, for instance as part of receiving second training set 220,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation using second graphical user interface as described above.

Referring now to FIG. 3, data incorporated in first training set 200and/or second training set 220 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a biologicalextraction database 300. A biological extraction database 300 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 300 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 300 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularbiological extractions that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastbiological extractions, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by diagnostic engine 108 in previous iterationsof methods, with or without validation of correctness by medicalprofessionals. Data entries in a biological extraction database 300 maybe flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a biological extraction and/or a person from whom abiological extraction was extracted or received with one or morecohorts, including demographic groupings such as ethnicity, sex, age,income, geographical region, or the like, one or more common diagnosesor physiological attributes shared with other persons having biologicalextractions reflected in other data entries, or the like. Additionalelements of information may include one or more categories ofphysiological data as described above. Additional elements ofinformation may include descriptions of particular methods used toobtain biological extractions, such as without limitation physicalextraction of blood samples or the like, capture of data with one ormore sensors, and/or any other information concerning provenance and/orhistory of data acquisition. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a biological extraction database 300 may reflectcategories, cohorts, and/or populations of data consistently with thisdisclosure.

Referring now to FIG. 4, one or more database tables in biologicalextraction database 300 may include, as a non-limiting example, aprognostic link table 400. Prognostic link table 400 may be a tablerelating biological extraction data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of biological extraction data and/or toan element of biological extraction data via first graphical userinterface 212 as described above, one or more rows recording such anentry may be inserted in prognostic link table 400. Alternatively oradditionally, linking of prognostic labels to biological extraction datamay be performed entirely in a prognostic label database as describedbelow.

With continued reference to FIG. 4, biological extraction database 300may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 300 may include a fluid sample table 404 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 300 may include asensor data table 408, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, biological extraction database 300 may include agenetic sample table 412, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, biological extractiondatabase 300 may include a medical report table 416, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 412, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, biological extraction database 300 mayinclude a tissue sample table 420, which may record biologicalextractions obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in biologicalextraction database 300 consistently with this disclosure.

Referring again to FIG. 3, diagnostic engine 108 and/or another devicein diagnostic engine 108 may populate one or more fields in biologicalextraction database 300 using expert information, which may be extractedor retrieved from an expert knowledge database 304. An expert knowledgedatabase 304 may include any data structure and/or data store suitablefor use as a biological extraction database 300 as described above.Expert knowledge database 304 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 2,including without limitation by using first graphical user interface 212and/or second graphical user interface 232. Expert knowledge databasemay include one or more fields generated by language processing module216, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data as described above may be stored ingeneralized from in an expert knowledge database 304 and linked to,entered in, or associated with entries in a biological extractiondatabase 300. Documents may be stored and/or retrieved by diagnosticengine 108 and/or language processing module 216 in and/or from adocument database 308; document database 308 may include any datastructure and/or data store suitable for use as biological extractiondatabase 300 as described above. Documents in document database 308 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 5, an exemplary embodiment of an expert knowledgedatabase 304 is illustrated. Expert knowledge database 304 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 304 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 300 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in expertknowledge database 304 may include, as a non-limiting example, an expertprognostic table 500. Expert prognostic table 500 may be a tablerelating biological extraction data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of biological extraction data and/or toan element of biological extraction data via first graphical userinterface 212 as described above, one or more rows recording such anentry may be inserted in expert prognostic table 500. In an embodiment,a forms processing module 504 may sort data entered in a submission viafirst graphical user interface 212 by, for instance, sorting data fromentries in the first graphical user interface 212 to related categoriesof data; for instance, data entered in an entry relating in the firstgraphical user interface 212 to a prognostic label may be sorted intovariables and/or data structures for storage of prognostic labels, whiledata entered in an entry relating to a category of physiological dataand/or an element thereof may be sorted into variables and/or datastructures for the storage of, respectively, categories of physiologicaldata or elements of physiological data. Where data is chosen by anexpert from pre-selected entries such as drop-down lists, data may bestored directly; where data is entered in textual form, languageprocessing module 216 may be used to map data to an appropriate existinglabel, for instance using a vector similarity test or othersynonym-sensitive language processing test to map physiological data toan existing label. Alternatively or additionally, when a languageprocessing algorithm, such as vector similarity comparison, indicatesthat an entry is not a synonym of an existing label, language processingmodule may indicate that entry should be treated as relating to a newlabel; this may be determined by, e.g., comparison to a threshold numberof cosine similarity and/or other geometric measures of vectorsimilarity of the entered text to a nearest existent label, anddetermination that a degree of similarity falls below the thresholdnumber and/or a degree of dissimilarity falls above the thresholdnumber. Data from expert textual submissions 508, such as accomplishedby filling out a paper or PDF form and/or submitting narrativeinformation, may likewise be processed using language processing module216. Data may be extracted from expert papers 512, which may includewithout limitation publications in medical and/or scientific journals,by language processing module 216 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure. Expert prognostic table500 may include a single table and/or a plurality of tables; pluralityof tables may include tables for particular categories of prognosticlabels such as a current diagnosis table, a future prognosis table, agenetic tendency table, a metabolic tendency table, and/or an endocrinaltendency table (not shown), to name a few non-limiting examplespresented for illustrative purposes only.

With continued reference to FIG. 5, one or more database tables inexpert knowledge database 304 may include, as a further non-limitingexample tables listing one or more ameliorative process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from secondgraphical user interface 232 via forms processing module 504 and/orlanguage processing module 216, processing of textual submissions 508,or processing of expert papers 512. For instance, and withoutlimitation, an ameliorative nutrition table 516 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 520 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 524 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 528 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 532 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database304 consistently with this disclosure.

Referring again to FIG. 3, a prognostic label database 312, which may beimplemented in any manner suitable for implementation of biologicalextraction database 300, may be used to store prognostic labels used indiagnostic engine 108, including any prognostic labels correlated withelements of physiological data in first training set 200 as describedabove; prognostic labels may be linked to or refer to entries inbiological extraction database 300 to which prognostic labelscorrespond. Linking may be performed by reference to historical dataconcerning biological extractions, such as diagnoses, prognoses, and/orother medical conclusions derived from biological extractions in thepast; alternatively or additionally, a relationship between a prognosticlabel and a data entry in biological extraction database 300 may bedetermined by reference to a record in an expert knowledge database 304linking a given prognostic label to a given category of biologicalextraction as described above. Entries in prognostic label database 312may be associated with one or more categories of prognostic labels asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 304.

Referring now to FIG. 6, an exemplary embodiment of a prognostic labeldatabase 312 is illustrated. Prognostic label database 312 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 312 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 312 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 6, one or more database tables in prognosticlabel database 312 may include, as a non-limiting example, an extractiondata table 600. Extraction data table 600 may be a table listing sampledata, along with, for instance, one or more linking columns to link suchdata to other information stored in prognostic label database 312. In anembodiment, extraction data 604 may be acquired, for instance frombiological extraction database 300, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 608, which may perform unit conversions. Datastandardization module 608 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 216 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 6, prognostic label database 312 may includean extraction label table 612; extraction label table 612 may listprognostic labels received with and/or extracted from biologicalextractions, for instance as received in the form of extraction text616. A language processing module 216 may compare textual information soreceived to prognostic labels and/or form new prognostic labelsaccording to any suitable process as described above. Extractionprognostic link table 620 may combine extractions with prognosticlabels, as acquired from extraction label table and/or expert knowledgedatabase 304; combination may be performed by listing together in rowsor by relating indices or common columns of two or more tables to eachother. Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in expert knowledge database 304 consistently with thisdisclosure.

Referring again to FIG. 3, first training set 200 may be populated byretrieval of one or more records from biological extraction database 300and/or prognostic label database 312; in an embodiment, entriesretrieved from biological extraction database 300 and/or prognosticlabel database 312 may be filtered and or select via query to match oneor more additional elements of information as described above, so as toretrieve a first training set 200 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom diagnostic engine 108 classifies biological extractions toprognostic labels as set forth in further detail below. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which records may be retrieved from biologicalextraction database 300 and/or prognostic label database to generate afirst training set to reflect individualized group data pertaining to aperson of interest in operation of system and/or method, includingwithout limitation a person with regard to whom at least a biologicalextraction is being evaluated as described in further detail below.Diagnostic engine 108 may alternatively or additionally receive a firsttraining set 200 and store one or more entries in biological extractiondatabase 300 and/or prognostic label database 312 as extracted fromelements of first training set 200.

Still referring to FIG. 3, diagnostic engine 108 may include orcommunicate with an ameliorative process label database 316; anameliorative process label database 316 may include any data structureand/or datastore suitable for use as a biological extraction database300 as described above. An ameliorative process label database 316 mayinclude one or more entries listing labels associated with one or moreameliorative processes as described above, including any ameliorativelabels correlated with prognostic labels in second training set 220 asdescribed above; ameliorative process labels may be linked to or referto entries in prognostic label database 312 to which ameliorativeprocess labels correspond. Linking may be performed by reference tohistorical data concerning prognostic labels, such as therapies,treatments, and/or lifestyle or dietary choices chosen to alleviateconditions associated with prognostic labels in the past; alternativelyor additionally, a relationship between an ameliorative process labeland a data entry in prognostic label database 312 may be determined byreference to a record in an expert knowledge database 304 linking agiven ameliorative process label to a given category of prognostic labelas described above. Entries in ameliorative process label database 312may be associated with one or more categories of prognostic labels asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 304.

Referring now to FIG. 7, an exemplary embodiment of an ameliorativeprocess label database 316 is illustrated. Ameliorative process labeldatabase 316 may, as a non-limiting example, organize data stored in theameliorative process label database 316 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 316 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 7, ameliorative process label database 316 mayinclude a prognostic link table 700; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label database 316 may include an ameliorativenutrition table 704, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 708 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 712 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 716 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 720 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 316 consistently withthis disclosure.

Referring again to FIG. 3, second training set 220 may be populated byretrieval of one or more records from prognostic label database 312and/or ameliorative process label database 316; in an embodiment,entries retrieved from prognostic label database 312 and/or ameliorativeprocess label database 316 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a second training set 220 including data belonging toa given cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom diagnostic engine 108 classifies prognostic labels toameliorative process labels as set forth in further detail below.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which records may beretrieved from prognostic label database 312 and/or ameliorative processlabel database 316 to generate a second training set 220 to reflectindividualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a biological extraction is being evaluatedas described in further detail below. Diagnostic engine 108 mayalternatively or additionally receive a second training set 220 andstore one or more entries in prognostic label database 312 and/orameliorative process label database 316 as extracted from elements ofsecond training set 220.

In an embodiment, and still referring to FIG. 3, diagnostic engine 108may receive an update to one or more elements of data represented infirst training set 200 and/or second training set 220, and may performone or more modifications to first training set 200 and/or secondtraining set 220, or to biological extraction database 300, expertknowledge database 304, prognostic label database 312, and/orameliorative process label database 316 as a result. For instance abiological extraction may turn out to have been erroneously recorded;diagnostic engine 108 may remove it from first training set 200, secondtraining set 220, biological extraction database 300, expert knowledgedatabase 304, prognostic label database 312, and/or ameliorative processlabel database 316 as a result. As a further example, a medical and/oracademic paper, or a study on which it was based, may be revoked;diagnostic engine 108 may remove it from first training set 200, secondtraining set 220, biological extraction database 300, expert knowledgedatabase 304, prognostic label database 312, and/or ameliorative processlabel database 316 as a result. Information provided by an expert maylikewise be removed if the expert loses credentials or is revealed tohave acted fraudulently.

Continuing to refer to FIG. 3, elements of data first training set 200,second training set 220, biological extraction database 300, expertknowledge database 304, prognostic label database 312, and/orameliorative process label database 316 may have temporal attributes,such as timestamps; diagnostic engine 108 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 200 and/or second training set 220, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring again to FIG. 2, diagnostic engine 108 may be configured torecord at least a biological extraction. At least a biologicalextraction may include a physically extracted sample, which as usedherein includes a sample obtained by removing and analyzing tissueand/or fluid. Physically extracted sample may include without limitationa blood sample, a tissue sample, a buccal swab, a mucous sample, a stoolsample, a hair sample, a fingernail sample, or the like. Physicallyextracted sample may include, as a non-limiting example, at least ablood sample. As a further non-limiting example, at least a biologicalextraction may include at least a genetic sample. At least a geneticsample may include a complete genome of a person or any portion thereof.At least a genetic sample may include a DNA sample and/or an RNA sample.At least a biological extraction may include an epigenetic sample, aproteomic sample, a tissue sample, a biopsy, and/or any other physicallyextracted sample. At least a biological extraction may include anendocrinal sample. As a further non-limiting example, the at least abiological extraction may include a signal from at least a sensorconfigured to detect physiological data of a user and recording the atleast a biological extraction as a function of the signal. At least asensor may include any medical sensor and/or medical device configuredto capture sensor data concerning a patient, including any scanning,radiological and/or imaging device such as without limitation x-rayequipment, computer assisted tomography (CAT) scan equipment, positronemission tomography (PET) scan equipment, any form of magnetic resonanceimagery (MM) equipment, ultrasound equipment, optical scanning equipmentsuch as photo-plethysmographic equipment, or the like. At least a sensormay include any electromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor may include a temperature sensor. At least a sensor mayinclude any sensor that may be included in a mobile device and/orwearable device, including without limitation a motion sensor such as aninertial measurement unit (IMU), one or more accelerometers, one or moregyroscopes, one or more magnetometers, or the like. At least a wearableand/or mobile device sensor may capture step, gait, and/or othermobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, and/or blood pressure. At least a sensor may be apart of diagnostic engine 108 or may be a separate device incommunication with diagnostic engine 108.

Still referring to FIG. 2, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a biological extraction consistent with thisdisclosure. At least a biological extraction may be added to biologicalextraction database 300.

With continued reference to FIG. 2, diagnostic engine 108 may include aprognostic label learner 236 operating on the diagnostic engine 108, theprognostic label learner 236 designed and configured to generate the atleast a prognostic output as a function of the first training set 200and the at least a biological extraction. Prognostic label learner 236may include any hardware and/or software module. Prognostic labellearner 236 is designed and configured to generate outputs using machinelearning processes. A machine learning process is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed by acomputing device/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 2, prognostic label learner 236 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 240 relating physiological statedata 204 to prognostic labels using the first training set 200 andgenerating the at least a prognostic output using the firstmachine-learning model 240; at least a first machine-learning model 240may include one or more models that determine a mathematicalrelationship between physiological state data 204 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithm used togenerate first machine-learning model 240 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naive Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 2, prognostic label learner 236 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 200; the trained network may then be used to apply detectedrelationships between elements of physiological state data 204 andprognostic labels.

Referring now to FIG. 8, machine-learning algorithms used by prognosticlabel learner 236 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 800 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data as inputs, prognostic labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of physiological data and prognostic labels;scoring function may, for instance, seek to maximize the probabilitythat a given element of physiological state data 204 and/or combinationof elements of physiological data is associated with a given prognosticlabel and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data 204 and/orcombination of elements of physiological state data 204 is notassociated with a given prognostic label and/or combination ofprognostic labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set200. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of physiological data and prognostic labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of prognostic labels, and/or are specified aslinked to a medical specialty and/or field of medicine covering aparticular set of prognostic labels. As a non-limiting example, aparticular set of blood test biomarkers and/or sensor data may betypically used by cardiologists to diagnose or predict variouscardiovascular conditions, and a supervised machine-learning process maybe performed to relate those blood test biomarkers and/or sensor data tothe various cardiovascular conditions; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate prognostic labels. Additional supervised learning processes maybe performed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships betweenphysiological data and prognostic labels.

Referring again to FIG. 2, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 804 executing ondiagnostic engine 108 and/or on another computing device incommunication with diagnostic engine 108, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, prognosticlabel learner 236 and/or diagnostic engine 108 may perform anunsupervised machine learning process on first training set 200, whichmay cluster data of first training set 200 according to detectedrelationships between elements of the first training set 200, includingwithout limitation correlations of elements of physiological state data204 to each other and correlations of prognostic labels to each other;such relations may then be combined with supervised machine learningresults to add new criteria for prognostic label learner 236 to apply inrelating physiological state data 204 to prognostic labels. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first element of physiological data acquired in a bloodtest correlates closely with a second element of physiological data,where the first element has been linked via supervised learningprocesses to a given prognostic label, but the second has not; forinstance, the second element may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample a close correlation between first element of physiological statedata 204 and second element of physiological state data 204 may indicatethat the second element is also a good predictor for the prognosticlabel; second element may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstphysiological element by prognostic label learner 236.

Still referring to FIG. 2, diagnostic engine 108 and/or prognostic labellearner 236 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to diagnostic engine 108,prognostic label learner 236 and/or diagnostic engine 108 maycontinuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable diagnosticengine 108 to use detected relationships to discover new correlationsbetween known biomarkers, prognostic labels, and/or ameliorative labelsand one or more elements of data in large bodies of data, such asgenomic, proteomic, and/or microbiome-related data, enabling futuresupervised learning and/or lazy learning processes as described infurther detail below to identify relationships between, e.g., particularclusters of genetic alleles and particular prognostic labels and/orsuitable ameliorative labels. Use of unsupervised learning may greatlyenhance the accuracy and detail with which system may detect prognosticlabels and/or ameliorative labels.

With continued reference to FIG. 2, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 2, prognostic label learner 236 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 200 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 808 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 200. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 236 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naive Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

In an embodiment, and continuing to refer to FIG. 2, prognostic labellearner 236 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a biological extraction includes a result of a dexterity test,a low score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 236 and/or diagnostic engine108 may perform additional processes to resolve ambiguity. Processes mayinclude presenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or biological extractions are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 236 and/ordiagnostic engine 108 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 236 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 812 may be provided to user output deviceas described in further detail below.

Still referring to FIG. 2, diagnostic engine 108 includes anameliorative process label learner 244 operating on the diagnosticengine 108, the ameliorative process label learner 244 designed andconfigured to generate the at least an ameliorative output as a functionof the second training set 220 and the at least a prognostic output.Ameliorative process label learner 244 may include any hardware orsoftware module suitable for use as a prognostic label learner 236 asdescribed above. Ameliorative process label learner 244 is amachine-learning module as described above; ameliorative process labellearner 244 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 236 asdescribed above. For instance, and without limitation, and ameliorativeprocess label learner 244 may be configured to create a secondmachine-learning model 248 relating prognostic labels to ameliorativelabels using the second training set 220 and generate the at least anameliorative output using the second machine-learning model 248; secondmachine-learning model 248 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,ameliorative process label learner 244 may use data from first trainingset 200 as well as data from second training set 220; for instance,ameliorative process label learner 244 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and ameliorative labels. Where ameliorative process label learner 244determines relationships between elements of physiological data andameliorative labels directly, this may determine relationships betweenprognostic labels and ameliorative labels as well owing to the existenceof relationships determined by prognostic label learner 236.

Referring now to FIG. 9, ameliorative process label learner 244 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 900 executing on diagnostic engine 108 and/oron another computing device in communication with diagnostic engine 108,which may include any hardware or software module. For instance, asupervised learning algorithm may use prognostic labels as inputs,ameliorative labels as outputs, and a scoring function representing adesired form of relationship to be detected between prognostic labelsand ameliorative labels; scoring function may, for instance, seek tomaximize the probability that a given prognostic label and/orcombination of prognostic labels is associated with a given ameliorativelabel and/or combination of ameliorative labels to minimize theprobability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 9, ameliorative process label learner244 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 904 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 244and/or diagnostic engine 108 may perform an unsupervised machinelearning process on second training set 220, which may cluster data ofsecond training set 220 according to detected relationships betweenelements of the second training set 220, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 244 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 208correlates closely with a second prognostic label 224, where the firstprognostic label 208 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 224 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 208 andsecond prognostic label 224 may indicate that the second prognosticlabel 224 is also a good match for the ameliorative label; secondprognostic label 224 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 208 by ameliorative process label learner 244.Unsupervised processes performed by ameliorative process label learner244 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 236 as described above.

Still referring to FIG. 9, diagnostic engine 108 and/or ameliorativeprocess label learner 244 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added todiagnostic engine 108, ameliorative process label learner 244 and/ordiagnostic engine 108 may continuously or iteratively performunsupervised machine-learning processes to detect relationships betweendifferent elements of the added and/or overall data; in an embodiment,this may enable diagnostic engine 108 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or ameliorative labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesto identify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable ameliorativelabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/orameliorative labels.

Continuing to view FIG. 9, ameliorative process label learner 244 may beconfigured to perform a lazy learning process as a function of thesecond training set 220 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 236. Lazy learning processes may be performed by a lazy learningmodule 908 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Ameliorative output 912 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 9, ameliorative processlabel learner 244 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 244and/or diagnostic engine 108 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, ameliorative process label learner 244 mayperform one or more lazy learning processes using a more comprehensiveset of user data to identify a more probably correct result of themultiple results. Results may be presented and/or retained withrankings, for instance to advise a medical professional of the relativeprobabilities of various ameliorative labels being correct or idealchoices for a given person; alternatively or additionally, ameliorativelabels associated with a probability of success or suitability below agiven threshold and/or ameliorative labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anadditional process may reveal that a person is allergic to tree nuts,and consumption of almonds may be eliminated as an ameliorative label tobe presented.

Continuing to refer to FIG. 9, ameliorative process label learner 244may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 916. As used herein,longitudinal data 916 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 916 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 916may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 244 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 916 may be added to ameliorative process database and/or secondtraining set.

Embodiments of diagnostic engine 108 may furnish augmented intelligencesystems that facilitate diagnostic, prognostic, curative, and/ortherapeutic decisions by medical professionals such as doctors.Diagnostic engine 108 may provide fully automated tools and resourcesfor each doctor to handle, process, diagnosis, develop treatment plans,facilitate and monitor all patient implementation, and record eachpatient status. Provision of expert system elements via expert inputsand document-driven language analysis may ensure that recommendationsgenerated by diagnostic engine 108 are backed by the very best medicalknowledge and practices in the world. Models and/or learners with accessto data in depth may enable generation of recommendations that aredirectly personalized for each patient, providing complete confidence,mitigated risk, and complete transparency. Access to well-organized andpersonalized knowledge in depth may greatly enhance efficiency ofmedical visits; in embodiments, a comprehensive visit may be completedin as little as 10 minutes. Recommendations may further suggest followup testing and/or therapy, ensuring an effective ongoing treatment andprognostic plan.

Referring again to FIG. 1, artificial intelligence advisory system 100includes a plan generation module 112 operating on the at least a server104. Plan generation module 112 may include any suitable hardware orhardware module. In an embodiment, plan generation module 112 isdesigned and configured to generate a comprehensive instruction set 116associated with the user based on the diagnostic output. In anembodiment, comprehensive instruction set 116 is a data structurecontaining instructions to be provided to the user to explain the user'scurrent prognostic status, as reflected by one or more prognosticoutputs and provide the user with a plan based on the at least anameliorative output, to achieve that. Comprehensive instruction set 116may include but is not limited to a program, strategy, summary,recommendation, or any other type of interactive platform that may beconfigured to comprise information associated with the user, anapplicable verified external source, and one or more outputs derivedfrom the analyses performed on the extraction from the user.Comprehensive instruction set 116 may describe to a user a futureprognostic status to aspire to.

Referring now to FIG. 10, an exemplary embodiment of a plan generationmodule 112 is illustrated. Comprehensive instruction set 116 includes atleast a current prognostic descriptor 1000 which as used in thisdisclosure is an element of data describing a current prognostic statusbased on at least one prognostic output. Plan generation module 112 mayproduce at least a current prognostic descriptor 1000 using at least aprognostic output. In an embodiment, plan generation module 112 mayinclude a label synthesizer 1004. Label synthesizer 1004 may include anysuitable software or hardware module. In an embodiment, labelsynthesizer 1004 may be designed and configured to combine a pluralityof labels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer1004 and/or at least a server 104 may be designed and configure todetermine a first prognostic label of the at least a prognostic label isa duplicate of a second prognostic label of the at least a prognosticlabel and eliminate the first prognostic label. Determination that afirst prognostic label is a duplicate of a second prognostic label mayinclude determining that the first prognostic label is identical to thesecond prognostic label; for instance, a prognostic label generated fromtest data presented in one biological extraction of at least abiological extraction may be the same as a prognostic label generatedfrom test data presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstprognostic label may be synonymous with a second prognostic label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 216 as described above.

Continuing to refer to FIG. 10, label synthesizer 1004 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, plangeneration module 112 and/or label synthesizer 1004 may be configured todetermine that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category. A shared category may be a category ofconditions or tendencies toward a future condition to which each offirst prognostic label and second prognostic label belongs; as anexample, lactose intolerance and gluten sensitivity may each be examplesof digestive sensitivity, for instance, which may in turn share acategory with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Plan generation module 112 may be configured to add acategory label associated with a shared category to comprehensiveinstruction set 116, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenprognostic labels and categories may be retrieved from a prognosticlabel classification database 1008, for instance by generating a queryusing one or more prognostic labels of at least a prognostic output,entering the query, and receiving one or more categories matching thequery from the prognostic label classification database 1008.

Referring now to FIG. 11, an exemplary embodiment of a prognostic labelclassification database 1008 is illustrated. Prognostic labelclassification database 1008 may be implemented as any database and/ordatastore suitable for use as biological extraction database 300 asdescribed above. One or more database tables in prognostic labelclassification database 1008 may include, without limitation, asymptomatic classification table 1100; symptomatic classification table1100 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1100 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database1008 may include, without limitation, a systemic classification table1104; systemic classification table 1104 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1104 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1104 to the immune system. One or moredatabase tables in prognostic label classification database 1008 mayinclude, without limitation, a body part classification table 1108; bodypart classification table 1108 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1108 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 1008 may include, without limitation, a causal classificationtable 1112; causal classification table 1112 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1112 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring again to FIG. 10, plan generation module 112 may be configuredto generate current prognostic descriptor 1000 by converting one or moreprognostic labels into narrative language. As a non-limiting example,plan generation module 112 may include a narrative language unit 1012,which may be configured to determine an element of narrative languageassociated with at least a prognostic label and include the element ofnarrative language in current prognostic label descriptor. Narrativelanguage unit 1012 may implement this, without limitation, by using alanguage processing module 216 to detect one or more associationsbetween prognostic labels, or lists of prognostic labels, and phrasesand/or statements of narrative language. Alternatively or additionally,narrative language unit 1012 may retrieve one or more elements ofnarrative language from a narrative language database 1016, which maycontain one or more tables associating prognostic labels and/or groupsof prognostic labels with words, sentences, and/or phrases of narrativelanguage. One or more elements of narrative language may be included incomprehensive instruction set 116, for instance for display to a user astext describing a current prognostic status of the user. Currentprognostic descriptor 1000 may further include one or more images; oneor more images may be retrieved by plan generation module 112 from animage database 1020, which may contain one or more tables associatingprognostic labels, groups of prognostic labels, current prognosticdescriptors 1000, or the like with one or more images.

With continued reference to FIG. 10, comprehensive instruction set 116may include one or more follow-up suggestions, which may include,without limitation, suggestions for acquisition of an additionalbiological extraction; in an embodiment, additional biologicalextraction may be provided to diagnostic engine 108, which may triggerrepetition of one or more processes as described above, includingwithout limitation generation of prognostic output, refinement orelimination of ambiguous prognostic labels of prognostic output,generation of ameliorative output, and/or refinement or elimination ofambiguous ameliorative labels of ameliorative output. For instance,where a pegboard test result suggests possible diagnoses of Parkinson'sdisease, Huntington's disease, ALS, and MS as described above, follow-upsuggestions may include suggestions to perform endocrinal tests, genetictests, and/or electromyographic tests; results of such tests mayeliminate one or more of the possible diagnoses, such that asubsequently displayed output only lists conditions that have not beeneliminated by the follow-up test. Follow-up tests may include anyreceipt of any biological extraction as described above.

With continued reference to FIG. 10, comprehensive instruction set mayinclude one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with diagnostic engine 108. Contextual information mayinclude one or more questions a patient wishes to have answered in amedical visit and/or session, and/or as a result of interaction withdiagnostic engine 108. Contextual information may include one or morequestions to ask a patient. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofcontextual information that may be included, consistently with thisdisclosure.

With continued reference to FIG. 10, comprehensive instruction set 116may include at least a future prognostic descriptor 1024. As usedherein, a future prognostic descriptor 1024 is an element of datadescribing a future prognostic status based on at least one prognosticoutput, which may include without limitation a desired furtherprognostic status. In an embodiment, future prognostic descriptor 1024may include any element suitable for inclusion in current prognosticdescriptor 1000. Future prognostic descriptor 1024 may be generatedusing any processes, modules, and/or components suitable for generationof current prognostic descriptor 1000 as described above.

Still referring to FIG. 10, Comprehensive instruction set 116 includesat least an ameliorative process descriptor 1028, which as defined inthis disclosure an element of data describing one or more ameliorativeprocesses to be followed based on at least one ameliorative output; atleast an ameliorative process descriptor 1028 may include descriptorsfor ameliorative processes usable to achieve future prognosticdescriptor 1024. Plan generation module 112 may produce at least anameliorative process descriptor 1028 using at least a prognostic output.In an embodiment, label synthesizer 1004 may be designed and configuredto combine a plurality of labels in at least an ameliorative outputtogether to provide maximally efficient data presentation. Combinationof labels together may include elimination of duplicate information. Forinstance, label synthesizer 1004 and/or at least a server 104 may bedesigned and configure to determine a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label and eliminate the firstameliorative label. Determination that a first ameliorative label is aduplicate of a second ameliorative label may include determining thatthe first ameliorative label is identical to the second ameliorativelabel; for instance, a ameliorative label generated from test datapresented in one biological extraction of at least a biologicalextraction may be the same as a ameliorative label generated from testdata presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstameliorative label may be synonymous with a second ameliorative label,where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 216 as described above.

Continuing to refer to FIG. 10, label synthesizer 1004 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, plangeneration module 112 and/or label synthesizer 1004 may be configured todetermine that a first ameliorative label of the at least anameliorative label and a second ameliorative label of the at least anameliorative label belong to a shared category. A shared category may bea category of conditions or tendencies toward a future condition towhich each of first ameliorative label and second ameliorative labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with ameliorative labels aswell. A given ameliorative label may belong to a plurality ofoverlapping categories. Plan generation module 112 may be configured toadd a category label associated with a shared category to comprehensiveinstruction set 116, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenameliorative labels and categories may be retrieved from an ameliorativelabel classification database 1032, for instance by generating a queryusing one or more ameliorative labels of at least an ameliorativeoutput, entering the query, and receiving one or more categoriesmatching the query from the ameliorative label classification database1032.

Referring now to FIG. 12, an exemplary embodiment of an ameliorativelabel classification database 1032 is illustrated. Ameliorative labelclassification database 1032 may be implemented as any database and/ordatastore suitable for use as biological extraction database 300 asdescribed above. One or more database tables in ameliorative labelclassification database 1032 may include, without limitation, anintervention category table 1200; intervention 1200 may relate eachameliorative label to one or more categories associated with thatameliorative label. As a non-limiting example, intervention categorytable 1200 may include records indicating that each of a plan to consumea given quantity of almonds and a plan to consume less meat maps to acategory of nutritional instruction, while a plan to jog for 30 minutesper day maps to a category of activity. One or more database tables inameliorative label classification database 1032 may include, withoutlimitation, a nutrition category table 1204; nutrition category table1204 may relate each ameliorative label pertaining to nutrition to oneor more categories associated with that ameliorative label. As anon-limiting example, nutrition category table 1204 may include recordsindicating that each of a plan to consume more almonds and a plan toconsume more walnuts qualifies as a plan to consume more nuts, as wellas a plan to consume more protein. One or more database tables inameliorative label classification database 1032 may include, withoutlimitation, an action category table 1208; action category table 1208may relate each ameliorative label pertaining to an action to one ormore categories associated with that ameliorative label. As anon-limiting example, action category table 1208 may include recordsindicating that each of a plan jog for 30 minutes a day and a plan toperform a certain number of sit-ups per day qualifies as an exerciseplan. One or more database tables in ameliorative label classificationdatabase 1032 may include, without limitation, a medication categorytable 1212; medication category table 1212 may relate each ameliorativelabel associated with a medication to one or more categories associatedwith that ameliorative label. As a non-limiting example, medicationcategory table 1212 may include records indicating that each of a planto take an antihistamine and a plan to take an anti-inflammatory steroidbelongs to a category of allergy medications. One or more databasetables in ameliorative label classification database 1032 may include,without limitation, a supplement category table 1216; supplementcategory table 1216 may relate each ameliorative label pertaining to asupplement to one or more categories associated with that ameliorativelabel. As a non-limiting example, supplement category table 1216 mayinclude records indicating that each of a plan to consume a calciumsupplement and a plan to consume a vitamin D supplement corresponds to acategory of supplements to aid in bone density. Ameliorative labels maybe mapped to each of nutrition category table 1204, action categorytable 1208, supplement category table 1216, and medication categorytable 1212 using intervention category table 1200. The above-describedtables, and entries therein, are provided solely for exemplary purposes.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples for tablesand/or relationships that may be included or recorded in ameliorativeclassification table consistently with this disclosure.

Referring again to FIG. 10, plan generation module 112 may be configuredto generate ameliorative process descriptor 1028 by converting one ormore ameliorative labels into narrative language. As a non-limitingexample, plan generation module 112 may include a narrative languageunit 1012, which may be configured to determine an element of narrativelanguage associated with at least an ameliorative label and include theelement of narrative language in current ameliorative label descriptor.Narrative language unit 1012 may implement this, without limitation, byusing a language processing module 216 to detect one or moreassociations between ameliorative labels, or lists of ameliorativelabels, and phrases and/or statements of narrative language.Alternatively or additionally, narrative language unit 1012 may retrieveone or more elements of narrative language from narrative languagedatabase 1016, which may contain one or more tables associatingameliorative labels and/or groups of ameliorative labels with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in comprehensive instruction set 116,for instance for display to a user as text describing a currentameliorative status of the user. Ameliorative process descriptor 1028may further include one or more images; one or more images may beretrieved by plan generation module 112 from an image database 1020,which may contain one or more tables associating ameliorative labels,groups of ameliorative labels, ameliorative process descriptors 1028, orthe like with one or more images.

Referring now to FIG. 13, and exemplary embodiment of a narrativelanguage database 1016 is illustrated. Narrative language database 1016may be implemented as any database and/or datastore suitable for use asbiological extraction database 300 as described above. One or moredatabase tables in narrative language database 1016 may include, withoutlimitation, a prognostic description table 1300, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 1016may include, without limitation, an ameliorative description table 1304,which may link ameliorative process labels to narrative descriptionsassociated with ameliorative process labels. One or more database tablesin narrative language database 1016 may include, without limitation, acombined description table 1308, which may link combinations ofprognostic labels and ameliorative labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 1016 may include, without limitation, aparagraph template table 1312, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from image database 1020 and text obtained fromprognostic description table 1300, ameliorative description table 1304,and combined description table 1308 may be inserted. Tables in narrativedescription table 1016 may be populated, as a non-limiting example,using submissions from experts, which may be collected according to anyprocesses described above. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various way sin whichentries in narrative description table 1016 may be categorized and/ororganized.

Referring now to FIG. 14, an exemplary embodiment of an image database1020 is illustrated. Image database 1020 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 300 as described above. One or more database tables in imagedatabase 102 may include, without limitation, a prognostic image table1400, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 1020may include, without limitation, an ameliorative image table 1404, whichmay link ameliorative process labels to images associated withameliorative process labels. One or more database tables in imagedatabase 1020 may include, without limitation, a combined descriptiontable 1408, which may link combinations of prognostic labels andameliorative labels to images associated with the combinations. One ormore database tables in image database 102 may include, withoutlimitation, a prognostic video table 1412, which may link prognosticlabels to videos associated with prognostic labels. One or more databasetables in image database 1020 may include, without limitation, anameliorative video table 1416, which may link ameliorative processlabels to videos associated with ameliorative process labels. One ormore database tables in image database 1020 may include, withoutlimitation, a combined video table 1420, which may link combinations ofprognostic labels and ameliorative labels to videos associated with thecombinations. Tables in image database 1020 may be populated, withoutlimitation, by submissions by experts, which may be provided accordingto any process or process steps described in this disclosure forcollection of expert submissions.

Referring again to FIG. 10, plan generation module 112 may be configuredto receive at least an element of user data and filter diagnostic outputusing the at least an element of user data. At least an element of userdata, as used herein, is any element of data describing the user, userneeds, and/or user preferences. At least an element of user data mayinclude a constitutional restriction. At least a constitutionalrestriction may include any health-based reason that a user may beunable to engage in a given ameliorative process; at least aconstitutional restriction may include any counter-indication asdescribed above, including an injury, a diagnosis of somethingpreventing use of one or more ameliorative processes, an allergy orfood-sensitivity issue, a medication that is counter-indicated, or thelike. At least an element of user data may include at least a userpreference. At least a user preference may include, without limitation,any preference to engage in or eschew any ameliorative process and/orother potential elements of a comprehensive instruction set 116,including religious preferences such as forbidden foods, medicalinterventions, exercise routines, or the like.

Referring to FIG. 15, an exemplary embodiment of a user database 1036 isillustrated. User database 1036 may be implemented as any databaseand/or datastore suitable for use as biological extraction database 300as described above. One or more database tables in user database 1036may include, without limitation, a constitution restriction table 1500;at least a constitutional restriction may be linked to a given userand/or user identifier in a constitutional restriction table 1500. Oneor more database tables in user database 1036 may include, withoutlimitation, a user preference table 1504; at least a user preference maybe linked to a given user and/or user identifier in a user preferencetable 1504.

Referring again to FIG. 1, artificial intelligence advisory systemincludes a client-interface module 120. Client-interface module 120 mayinclude any suitable hardware or software module. Client-interfacemodule 120 is designed and configured to transmit comprehensiveinstruction set 116 to at least a user client device 124 associated withthe user. A user client device 124 may include, without limitation, adisplay in communication with diagnostic engine 108; display may includeany display as described below in reference to FIG. 20. A user clientdevice 124 may include an addition computing device, such as a mobiledevice, laptop, desktop computer, or the like; as a non-limitingexample, the user client device 124 may be a computer and/or workstationoperated by a medical professional. Output may be displayed on at leasta user client device 124 using an output graphical user interface;output graphical user interface may display at least a currentprognostic descriptor 1000, at least a future prognostic descriptor1024, and/or at least an ameliorative process descriptor 1028.

With continued reference to FIG. 1, artificial intelligence advisorysystem includes at least an advisory module executing on the at least aserver 104. At least an advisory module may include any suitablehardware or software module. In an embodiment, at least an advisorymodule is designed and configured to generate at least an advisoryoutput as a function of the comprehensive instruction set 116 andtransmit the advisory output to at least an advisor client device 132.At least an advisor client device 132 may include any device suitablefor use as a user client device 124 as described above. At least anadvisor client device 132 may be a user client device 124 as describedabove; that is, at least an advisory output may be output to the userclient device 124. Alternatively or additionally, at least an advisorclient device 132 may be operated by an informed advisor, defined forthe purposes of this disclosure as any person besides the user who hasaccess to information useable to aid user in interaction with artificialintelligence advisory system. An informed advisor may include, withoutlimitation, a medical professional such as a doctor, nurse, nursepractitioner, functional medicine practitioner, any professional with acareer in medicine, nutrition, genetics, fitness, life sciences,insurance, and/or any other applicable industry that may contributeinformation and data to system 100 regarding medical needs. An informedadvisor may include a spiritual or philosophical advisor, such as areligious leader, pastor, imam, rabbi, or the like. An informed advisormay include a physical fitness advisor, such as without limitation apersonal trainer, instructor in yoga or martial arts, sports coach, orthe like.

Referring now to FIG. 16, an exemplary embodiment of an advisory module128 is illustrated. Advisory module 128 may be configured to generate anadvisor instruction set 1600 as a function of the diagnostic output.Advisory instruction set 1600 may contain any element suitable forinclusion in comprehensive instruction set 116; advisory instruction set1600 and/or any element thereof may be generated using any processsuitable for generation of comprehensive instruction set 116. Advisoryinstruction set 1600 may include one or more specialized instructions1604; specialized instructions, as used herein, are instructions thecontents of which are selected for display to a particular informedadvisor. Selection of instructions for a particular informed advisor maybe obtained, without limitation, from information concerning theparticular informed advisor, which may be retrieved from a user database1036 or the like. As a non-limiting example, where an informed advisoris a doctor, specialized instruction 1604 may include data frombiological extraction as described above; specialized instruction mayinclude one or more medical records of user, which may, as anon-limiting example, be downloaded or otherwise received from anexternal database containing medical records and/or a database (notshown) operating on at least a server 104. As a further non-limitingexample medical data relevant to fitness, such as orthopedic reports,may be provided to an informed advisor whose role is as a fitnessinstructor, coach, or the like. Information provided to informedadvisors may be extracted or received from any database describedherein, including without limitation biological extraction database 300.

In an embodiment, and continuing to refer to FIG. 16, advisory module128 may be configured to receive at least an advisory input from theadvisor client device 132. At least an advisory input may include anyinformation provided by an informed advisor via advisor client device132. Advisory input may include medical information and/or advice.Advisory input may include user data, including user habits,preferences, religious affiliations, constitutional restrictions, or thelike. Advisory input may include spiritual and/or religious advice.Advisory input may include user-specific diagnostic information.Advisory input may be provided to user client device 124; alternativelyor additionally, advisory input may be fed back into system 100,including without limitation insertion into user database 1036,inclusion in or use to update diagnostic engine 108, for instance byaugmenting machine-learning models and/or modifying machine-learningoutputs via a lazy-learning protocol or the like as described above.

With continued reference to FIG. 16, advisory module 128 may include anartificial intelligence advisor 1608 configured to perform a usertextual conversation with the user client device 124. Artificialintelligence advisor 1608 may provide output to advisor client device132 and/or user client device 124. Artificial intelligence advisor 1608may receive inputs from advisor client device 132 and/or user clientdevice 124. Inputs and/or outputs may be exchanged using messagingservices and/or protocols, including without limitation any instantmessaging protocols. Persons skilled in the art, up reviewing theentirety of this disclosure, will be aware of a multiplicity ofcommunication protocols that may be employed to exchange text messagesas described herein. Text messages may be provided in textual formand/or as audio files using, without limitation, speech-to-text and/ortext-to-speech algorithms.

Referring now to FIG. 17, an exemplary embodiment of an artificialintelligence advisor 1608 is illustrated. Artificial intelligenceadvisor 1608 may include a user communication learner 1700. Usercommunication learner 1700 may be any form of machine-learning learneras described above, implementing any form of language processing and/ormachine learning. In an embodiment, user communication learner 1700 mayinclude a general learner 1704; general learner 1704 may be a learnerthat derives relationships between user inputs and correct outputs usinga training set that includes, without limitation, a corpus of previousconversations. Corpus of previous conversations may be logged by atleast a server 104 as conversations take place; user feedback, and/orone or more functions indicating degree of success of a conversation maybe used to differentiate between positive input-output pairs to use fortraining and negative input-output pairs not to use for training.Outputs may include textual strings and/or outputs from any databases,modules, and/or learners as described in this disclosure, includingwithout limitation prognostic labels, prognostic descriptors,ameliorative labels, ameliorative descriptors, user information, or thelike; for instance, general learner 1704 may determine that some inputsoptimally map to textual response outputs, while other inputs map tooutputs created by retrieval of module and/or database outputs, such asretrieval of prognostic descriptors, ameliorative descriptors, or thelike. User communication learner may include a user-specific learner1708, which may generate one or more modules that learn input-outputpairs pertaining to communication with a particular user; a userspecific learner 1708 may initially use input-output pairs establishedby general learner 1704 and may modify such pairs to match optimalconversation with the particular user by iteratively minimizing an errorfunction.

Still referring to FIG. 17, general learner 1704 and/or user-specificlearner 1708 may initialize, prior to training, using one or more recordretrieved from a default response database 1712. Default responsedatabase 1712 may link inputs to outputs according to initialrelationships entered by users, including without limitation experts asdescribed above, and/or as created by a previous instance or version ofgeneral learner 1704 and/or user-specific learner 1708. Default responsedatabase 1712 may periodically be updated with information from newlygenerated instances of general learner 1704 and/or user-specific learner1708. Inputs received by artificial intelligence advisor 1608 may bemapped to canonical and/or representative inputs by synonym detection asperformed, for instance, by a language processing module 216; languageprocessing module 216 may be involved in textual analysis and/orgeneration of text at any other point in machine-learning and/orcommunication processes undergone by artificial intelligence advisor1608.

Referring now to FIG. 18, an exemplary embodiment of a default responsedatabase 1712 is illustrated. Default response database 1712 may beimplemented as any database and/or datastore suitable for use asbiological extraction database 300 as described above. One or moredatabase tables in default response database 1712 may include, withoutlimitation, an input/output table 1800, which may link default inputs todefault outputs. Default response database 1712 may include a user table1804, which may, for instance, map users and/or a user client device 124to particular user-specific learners and/or past conversations. Defaultresponse database 1712 may include a user preference table 1808 listingpreferred modes of address, turns of phrase, or other user-specificcommunication preferences. Default response database 1712 may include ageneral preference table 1812, which may track, for instance,output-input pairings associated with greater degrees of usersatisfaction.

Referring again to FIG. 17, artificial intelligence advisor may includea consultation initiator configured to detect a consultation event in auser textual conversation and initiate a consultation with an informedadvisor as a function of the consultation event. A consultation event,as used herein, is a situation where an informed advisor is needed toaddress a user's situation or concerns, such as when a user should beconsulting with a doctor regarding an apparent medical emergency or newcondition, or with an advisor who can lend emotional support whenparticularly distraught. Detection may be performed, without limitation,by matching an input and/or set of inputs to an output that constitutesan action of initiating a consultation; such a pairing of an inputand/or input set may be learned using a machine learning process, forinstance via general learner and/or user specific learner 1708. In thelatter case, information concerning a particular user's physical oremotional needs or condition may be a part of the training set used togenerate the input/input set to consultation event pairing; forinstance, a user with a history of heart disease may triggerconsultation events upon any inputs describing shortness of breath,chest discomfort, arrhythmia, or the like. Initiation of consultationmay include transmitting a message to an advisor client device 132associated with an appropriate informed advisor, such as withoutlimitation transmission of information regarding a potential medicalemergency to a doctor able to assist in treating the emergency.Initiation of consultation may alternatively or additionally includeproviding an output to the user informing the user that a consultationwith an informed advisor, who may be specified by name or role, isadvisable.

Referring now to FIG. 19, an exemplary embodiment of a method 1900 ofvibrant constitutional guidance using artificial intelligence isillustrated. At step 1905. A diagnostic engine 108 operating on at leasta server 104 receives at least a biological extraction from a user; thismay be implemented, without limitation, as described above in referenceto FIGS. 1-18. At step 1910 diagnostic engine 108 generates a diagnosticoutput the diagnostic output including at least a prognostic label andat least an ameliorative process label; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-18. At step 1915a plan generation module 112 operating on the at least a server 104generates, based on the diagnostic output, a comprehensive instructionset 116 associated with the user, the comprehensive instruction set 116including at least an ameliorative process descriptor and at least anameliorative process descriptor; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-18. For instance,and without limitation, generating comprehensive instruction set 116 mayinclude determining a first prognostic label of the at least aprognostic label is a duplicate of a second prognostic label of the atleast a prognostic label and eliminating the first prognostic label. Asa further example, generating comprehensive instruction set 116 mayinclude determining that a first prognostic label of the at least aprognostic label and a second prognostic label of the at least aprognostic label belong to a shared category and adding a category labelassociated with the shared category to the comprehensive instruction set116. As an additional example, generating the comprehensive instructionset 116 may include determining a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label, and eliminating the firstprognostic label. As an additional example, generating comprehensiveinstruction set 116 may include determining that a first ameliorativelabel of the at least an ameliorative label and a second ameliorativelabel of the at least an ameliorative label belong to a shared categoryand adding a category label associated with the shared category to thecomprehensive instruction set 116. Generating comprehensive instructionset 116 may include, as a further example, receiving at least an elementof user data and filtering the diagnostic output using the at least anelement of user data.

At step 1920, a client-interface module 120 operating on at least aserver 104 transmits the comprehensive instruction set 116 to at least auser client device 124 associated with the user; this may beimplemented, without limitation, as described above in reference toFIGS. 1-18. At step 1925 at least an advisory module operating on the atleast a server 104 generates at least an advisory output as a functionof the comprehensive instruction set 116; this may be implemented,without limitation, as described above in reference to FIGS. 1-18

Systems and methods described herein may provide improvements to theprocessing, storage, and utility of data collected along with acentralized vibrant constitutional advice network configured to developcomprehensive plans for users, and execute processes and services basedon components of the comprehensive plans. By using a rule-based model ora machine-learned model to generate feature values of data containedwithin the collected data, one or more analyses are performed on thefeature values, and outputs of training data are generated and includedin an optimized set of data. The optimized set of data is used togenerate the comprehensive plans, and the vibrant constitutional advicenetwork is able to provide users with not only a method of acquiringdetailed genetic and physiological information, but more importantly theability to make decisions that support vibrant health and longevityinfluenced by the plurality of information based on the collected data.Furthermore, the systems and methods provide an unconventional use ofthe plurality of collected data via automatic execution of processes andservices by the vibrant constitutional advice network based on thegenerated comprehensive plans. Thus, the systems and methods describedherein improve the functioning of computing systems by optimizing bigdata processing and improving the utility of the processed big data viaits unconventional application, but most importantly the system andmethods improve overall health and lifestyle via the centralizedplatform promoting vibrant life and longevity.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 20 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 2000 includes a processor 2004 and a memory2008 that communicate with each other, and with other components, via abus 2012. Bus 2012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 2008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2016 (BIOS), including basic routines thathelp to transfer information between elements within computer system2000, such as during start-up, may be stored in memory 2008. Memory 2008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2000 may also include a storage device 2024. Examples ofa storage device (e.g., storage device 2024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2024 may beconnected to bus 2012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2024 (or one or more components thereof) may be removably interfacedwith computer system 2000 (e.g., via an external port connector (notshown)). Particularly, storage device 2024 and an associatedmachine-readable medium 2028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2000. In one example,software 2020 may reside, completely or partially, withinmachine-readable medium 2028. In another example, software 2020 mayreside, completely or partially, within processor 2004.

Computer system 2000 may also include an input device 2032. In oneexample, a user of computer system 2000 may enter commands and/or otherinformation into computer system 2000 via input device 2032. Examples ofan input device 2032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2032may be interfaced to bus 2012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2012, and any combinations thereof. Input device 2032may include a touch screen interface that may be a part of or separatefrom display 2036, discussed further below. Input device 2032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2000 via storage device 2024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2040. A networkinterface device, such as network interface device 2040, may be utilizedfor connecting computer system 2000 to one or more of a variety ofnetworks, such as network 2044, and one or more remote devices 2048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2020, etc.) may be communicated to and/or fromcomputer system 2000 via network interface device 2040.

Computer system 2000 may further include a video display adapter 2052for communicating a displayable image to a display device, such asdisplay device 2036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2052 and display device 2036 maybe utilized in combination with processor 2004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2012 via a peripheral interface 2056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,and systems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An artificial intelligence advisory system forvibrant constitutional guidance, the artificial intelligence advisorysystem comprising: at least a server; a diagnostic engine operating onthe at least a server, wherein the diagnostic engine is configured to:receive at least a biological extraction from a user; and generate adiagnostic output based on the at least a biological extraction, thediagnostic output including a plurality of prognostic labels and atleast an ameliorative process label; a plan generation module operatingon the at least a server, the plan generation module designed andconfigured to generate, based on the diagnostic output, a comprehensiveinstruction set associated with the user, wherein the comprehensiveinstruction set includes at least a current prognostic descriptor and atleast an ameliorative process descriptor, wherein generating thecomprehensive instruction set further includes: adding a prognosticcategory label to the comprehensive instruction set, wherein adding theprognostic category label further comprises: generating a query using afirst prognostic label of the plurality of prognostic labels and asecond prognostic label of the plurality of prognostic labels; queryinga prognostic label classification database using the query; receiving,as a result of the query, a first category of symptoms relating to thefirst prognostic label and a second category of symptoms relating to thesecond prognostic label; determining, using the first category ofsymptoms and the second category of symptoms, that the second prognosticlabel and the third prognostic label belong to a shared category; addinga category label associated with a shared category to the comprehensiveinstruction set; and adding a first prognostic narrative languageelement and a second prognostic narrative language element to thecomprehensive instruction set, wherein adding the first prognosticnarrative language element and the second prognostic narrative languageelement further comprises: determining a first element of narrativelanguage including a phrase associated with the first prognostic label,wherein determining further comprises detecting, within a corpus ofdocuments received from at least an expert and using a first hiddenMarkov model, a relationship between the phrase associated with thefirst prognostic label and the first prognostic label; determining asecond element of narrative language including a phrase associated withthe second prognostic label wherein determining further comprisesdetecting, within the corpus of documents and using a second hiddenMarkov model, a relationship between the phrase associated with thesecond prognostic label and the second prognostic label; and adding thefirst element of narrative language and the second element of narrativelanguage to the comprehensive instruction set; a client-interfacemodule, the client-interface module designed and configured to transmitthe comprehensive instruction set to at least a user client deviceassociated with the user; and at least an advisory module, the at leastan advisory module designed and configured to: generate at least anadvisory output as a function of the comprehensive instruction set; andtransmit the advisory output to at least an advisor client device. 2.The artificial intelligence advisory system of claim 1, wherein the plangeneration module is further configured to: determine that a firstprognostic label of the at least a prognostic label is a duplicate of asecond prognostic label of the at least a prognostic label; andeliminate the first prognostic label.
 3. The artificial intelligenceadvisory system of claim 1, wherein the plan generation module isfurther configured to: determine that a first prognostic label of the atleast a prognostic label and a second prognostic label of the at least aprognostic label belong to a shared category; and add a category labelassociated with the shared category to the comprehensive instructionset.
 4. The artificial intelligence advisory system of claim 1, whereinthe plan generation module is further configured to: determine that afirst ameliorative label of the at least an ameliorative label is aduplicate of a second ameliorative label of the at least an ameliorativelabel; and eliminate the first ameliorative label.
 5. The artificialintelligence advisory system of claim 1, wherein the plan generationmodule is further configured to: determine that a first ameliorativelabel of the at least an ameliorative label and a second ameliorativelabel of the at least an ameliorative label belong to a shared category;and add a category label associated with the shared category to thecomprehensive instruction set.
 6. The artificial intelligence advisorysystem of claim 1, wherein the plan generation module is furtherconfigured to: receive at least an element of user data; and filter thediagnostic output using the at least an element of user data.
 7. Theartificial intelligence advisory system of claim 6, wherein the at leastan element of user data further comprises a constitutional restriction.8. The artificial intelligence advisory system of claim 6, wherein theat least an element of user data further comprises a user preference. 9.The artificial intelligence advisory system of claim 1, wherein theclient-interface module is further configured to receive at least anelement of user data from the user client device.
 10. The artificialintelligence advisory system of claim 1, wherein the client-interfacemodule is further configured to receive at least an element of user datafrom the advisor client device.
 11. The artificial intelligence advisorysystem of claim 1, wherein the advisory module is configured to generatean advisor instruction set as a function of the diagnostic output. 12.The artificial intelligence advisory system of claim 1, wherein theadvisory module is configured to receive at least an advisory input fromthe advisor client device.
 13. The artificial intelligence advisorysystem of claim 1, wherein the advisory module further comprises anartificial intelligence advisor configured to perform a user textualconversation with the user client device.
 14. The artificialintelligence advisory system of claim 13, wherein the artificialintelligence advisor further comprises a consultation initiatorconfigured to: detect a consultation event in the user textualconversation; and initiate a consultation with an informed advisor as afunction of the consultation event.
 15. A method of vibrantconstitutional guidance using artificial intelligence, the methodcomprising: receiving, by a diagnostic engine operating on at least aserver, at least a biological extraction from a user; generating, by thediagnostic engine, a diagnostic output based on the at least abiological extraction, the diagnostic output including at least aprognostic label and at least an ameliorative process label; generating,by a plan generation module operating on the at least a server, andbased on the diagnostic output, a comprehensive instruction setassociated with the user, the comprehensive instruction set including atleast an ameliorative process descriptor and at least an ameliorativeprocess descriptor, wherein generating the comprehensive instruction setfurther includes: adding a prognostic category label to thecomprehensive instruction set, wherein adding the prognostic categorylabel further comprises: generating a query using a first prognosticlabel of the plurality of prognostic labels and a second prognosticlabel of the plurality of prognostic labels; querying a prognostic labelclassification database using the query; receiving, as a result of thequery, a first category of symptoms relating to the first prognosticlabel and a second category of symptoms relating to the secondprognostic label; determining, using the first category of symptoms andthe second category of symptoms, that the second prognostic label andthe third prognostic label belong to a shared category; adding acategory label associated with a shared category to the comprehensiveinstruction set; and adding a first prognostic narrative languageelement and a second prognostic narrative language element to thecomprehensive instruction set, wherein adding the first prognosticnarrative language element and the second prognostic narrative languageelement further comprises: determining a first element of narrativelanguage including a phrase associated with the first prognostic label,wherein determining further comprises detecting, within a corpus ofdocuments received from at least an expert and using a first hiddenMarkov model, a relationship between the phrase associated with thefirst prognostic label and the first prognostic label; determining asecond element of narrative language including a phrase associated withthe second prognostic label wherein determining further comprisesdetecting, within the corpus of documents and using a second hiddenMarkov model, a relationship between the phrase associated with thesecond prognostic label and the second prognostic label; and adding thefirst element of narrative language and the second element of narrativelanguage to the comprehensive instruction set; transmitting, by aclient-interface module operating on the at least a server, thecomprehensive instruction set to at least a user client deviceassociated with the user; and generating, by at least an advisorymodule, at least an advisory output as a function of the comprehensiveinstruction set.
 16. The method of claim 15, wherein generating thecomprehensive instruction set further comprises: determining that afirst prognostic label of the at least a prognostic label is a duplicateof a second prognostic label of the at least a prognostic label; andeliminating the first prognostic label.
 17. The method of claim 15,wherein generating the comprehensive instruction set further comprises:determining that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category; and adding a category label associated withthe shared category to the comprehensive instruction set.
 18. The methodof claim 15, wherein generating the comprehensive instruction setfurther comprises: determining that a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label; and eliminating the firstameliorative label.
 19. The method of claim 15, wherein generating thecomprehensive instruction set further comprises: determining that afirst ameliorative label of the at least an ameliorative label and asecond ameliorative label of the at least an ameliorative label belongto a shared category; and adding a category label associated with theshared category to the comprehensive instruction set.
 20. The method ofclaim 15, wherein generating the comprehensive instruction set furthercomprises: receiving at least an element of user data; and filtering thediagnostic output using the at least an element of user data.